Tuesday, September 26, 2017

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The IMpact of the Internet on Institutions in the FuturePew Internet & American Life Project

April, 2010

By an overwhelming margin, technology experts and stakeholders participating in a survey fielded by the Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center believe that innovative forms of online cooperation could result in more efficient and responsive for-profit firms, non-profit organizations, and government agencies by the year 2020.

A highly engaged set of respondents that included 895 technology stakeholders and critics participated in the online, opt-in survey. In this canvassing of a diverse number of experts, 72% agreed with the statement:

“By 2020, innovative forms of online cooperation will result in significantly more efficient and responsive governments, business, non-profits, and other mainstream institutions.”

Some 26% agreed with the opposite statement, which posited:

“By 2020, governments, businesses, non-profits and other mainstream institutions will primarily retain familiar 20th century models for conduct of relationships with citizens and consumers online and offline.”

While their overall assessment anticipates that humans’ use of the internet will prompt institutional change, many elaborated with written explanations that expressed significant concerns over organization’s resistance to change. They cited fears that bureaucracies of all stripes – especially government agencies – can resist outside encouragement to evolve. Some wrote that the level of change will affect different kinds of institutions at different times. The consensus among them was that businesses will transform themselves much more quickly than public and non-profit agencies. Read more...

About the Survey

The survey results are based on a non-random online sample of 895 internet experts and other internet users, recruited via email invitation, Twitter or Facebook from the Pew Research Center’s Internet & American Life Project and Elon University. Since the data are based on a non-random sample, a margin of error cannot be computed, and the results are not projectable to any population other than the experts in this sample.